Randomized numerical linear algebra (RNLA) is a recently developed technique for reducing the dimensions of matrices on which linear algebra operations are performed, by using random sampling. For example, “matrix sketching” can include multiplying a matrix by a pseudo random matrix so as to reduce the dimension of the matrix in a linear algebra operation, while retaining important information within the matrix. RNLA techniques can include the matrix multiplication of a wide pseudo random matrix times a tall measurement or data matrix. However, when matrix dimensions can be on the order of 1000s by 100000s or more, multiplying matrices can take a significant amount of computational time.